Decision Agents
Maximize trust, and minimize hallucinations with verifiable, cited Generative AI (GenAI) answers backed by state of the art causal models.
Before Causal Grounding
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Cannot reliably answer questions about quantitative enterprise data
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Unclear how answers are generated and how to reproduce outputs
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No citations to link answers to underlying data or models
After Causal Grounding
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Causal models enabling quantitative assessment of what-if scenarios
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Reproducible code snippets outlining how calculations were performed
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Clear citations referencing the underlying data or models
Answer Quantitative Questions
Ground Generative AI solutions in causal models enabling them to effectively answer questions about your tabular and time series challenges.
Causal models enable you to go beyond predictive insights, meaning that you can perform advanced what-if scenario modelling, root cause analysis and effect estimation in pure natural language.
Clear Citations
Generated answers are backed by clear citations which provide both a natural language explanation of the results for non-technical stakeholders, as well as a reproducible code snippet for analysts and data scientists.
These citations build trust in the generated answers, speeding business adoption of the solution.
Verifiable Code Snippets
Whenever your Gen AI model relies on the Causal AI grounding you will receive code snippets.
These code snippets outline exactly which calculations were performed, and enable you to reproduce the results in the future without relying on you remembering the original question.
Agents To Help You Build Too
Ways to get started
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Read our white papers, case studies, and research to understand how you can make the most of causal AI agents.
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